U.S. patent application number 14/513526 was filed with the patent office on 2015-04-23 for road weather hazard system.
This patent application is currently assigned to UNIVERSITY CORPORATION FOR ATMOSPHERIC RESEARCH. The applicant listed for this patent is UNIVERSITY CORPORATION FOR ATMOSPHERIC RESEARCH. Invention is credited to Amanda Anderson, Crystal Burghardt, Michael Chapman, Sheldon Drobot, Seth Linden, Gerry Wiener.
Application Number | 20150109146 14/513526 |
Document ID | / |
Family ID | 52825700 |
Filed Date | 2015-04-23 |
United States Patent
Application |
20150109146 |
Kind Code |
A1 |
Drobot; Sheldon ; et
al. |
April 23, 2015 |
ROAD WEATHER HAZARD SYSTEM
Abstract
A method and system for assessing road conditions is provided.
The method includes determining a road hazard condition for a road
segment that may include a precipitation type, a pavement
condition, and a visibility level. The precipitation type may be
determined using radar data, satellite cloud classification data,
weather station air temperature data, wiper status, mobile air
data, speed ratio, or headlight status. The pavement condition may
be determined using pavement temperature, precipitation type,
automatic brake system status, traction status or a stability
control observation, and a yaw rate. The visibility level may be
determined using wind speed, relative humidity, percentage of fog
lights on, percentage of high beams on, speed ratio, station
visibility, station-reported visibility type, wildfire existence,
wind direction, a dust existence indicator.
Inventors: |
Drobot; Sheldon; (Boulder,
CO) ; Chapman; Michael; (Arvada, CO) ;
Anderson; Amanda; (Boulder, CO) ; Wiener; Gerry;
(Boulder, CO) ; Linden; Seth; (Westminster,
CO) ; Burghardt; Crystal; (Evergreen, CO) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
UNIVERSITY CORPORATION FOR ATMOSPHERIC RESEARCH |
Boulder |
CO |
US |
|
|
Assignee: |
UNIVERSITY CORPORATION FOR
ATMOSPHERIC RESEARCH
Boulder
CO
|
Family ID: |
52825700 |
Appl. No.: |
14/513526 |
Filed: |
October 14, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61893653 |
Oct 21, 2013 |
|
|
|
Current U.S.
Class: |
340/905 |
Current CPC
Class: |
G08G 1/0133 20130101;
G08G 1/0112 20130101; G08G 1/096716 20130101; G08G 1/096775
20130101; G08G 1/0116 20130101; G01W 1/14 20130101; G08G 1/0141
20130101; G08G 1/012 20130101 |
Class at
Publication: |
340/905 |
International
Class: |
G08G 1/0967 20060101
G08G001/0967 |
Goverment Interests
GOVERNMENT LICENSE RIGHTS
[0002] This invention was made with Government support under
contract number DTFH61-08-D-00012 awarded by the U.S. Department of
Transportation. The Government has certain rights in the invention.
Claims
1. A method for evaluating a road hazard condition for a road
segment, the method comprising the steps of: receiving remote
weather data for the road segment; determining a precipitation type
for the road segment using the remote weather data; and determining
the road hazard condition for the road segment using the
precipitation type.
2. The method of claim 1, wherein the remote weather data includes
at least one of radar data, satellite cloud classification data,
and weather station air temperature data.
3. The method of claim 1, further comprising the step of: receiving
first mobile data for the road segment, wherein determining the
precipitation type for the road segment further includes using the
first mobile data.
4. The method of claim 3, wherein the first mobile data includes at
least one of a wiper status and a mobile air data.
5. The method of claim 3, further comprising the step of: receiving
second mobile data, wherein determining the precipitation type for
the road segment further includes using the second mobile data.
6. The method of claim 1, further comprising the steps of:
determining if a pavement temperature is received; in response to
determining that a pavement temperature is received, determining a
pavement condition for the road segment using the precipitation
type and the pavement temperature; and in response to determining
that the pavement temperature is not received, determining the
pavement condition for the road segment using the precipitation
type, wherein determining the road hazard condition for the road
segment further includes using the pavement condition.
7. The method of claim 6, further comprising the steps of:
receiving vehicle drive information; and determining a slickness
flag for the road segment using the vehicle drive information, the
precipitation type, and the pavement condition.
8. The method of claim 7, wherein the vehicle drive information
includes at least one of an automatic brake system status, a
traction status or a stability control observation, and a yaw
rate.
9. The method of claim 1, further comprising the step of:
determining a visibility level for the road segment using the
precipitation type, wherein determining the road hazard condition
for the road segment further includes using the visibility
level.
10. The method of claim 9, further comprising the step of:
receiving a wind speed, wherein determining the visibility level
for the road segment further includes using the wind speed.
11. The method of claim 9, further comprising the step of:
receiving visibility information, wherein determining the
visibility level for the road segment further includes using the
visibility information.
12. The method of claim 11, wherein the visibility information
includes at least one of a relative humidity, a percentage of fog
lights on, a percentage of high beams on, a speed ratio, a station
visibility, a station-reported visibility type, a wildfire
existence, a wind direction, and a dust existence indicator.
13. The method of claim 11, further comprising the step of:
receiving automobile operation information, wherein determining the
visibility level for the road segment further includes using the
automobile operation information.
14. A system for assessing a road hazard condition for a road
segment, the system comprising: a precipitation type module to
receive remote weather data for the road segment, to determine a
precipitation type for the road segment using the remote weather
data, and to determine the road hazard condition for the road
segment using the precipitation type.
15. The system claim 14, wherein the remote weather data includes
at least one of radar data, satellite cloud classification data,
and weather station air temperature data.
16. The system of claim 14, further comprising: a pavement
condition module to determine if a pavement temperature is
received, in response to determining that a pavement temperature is
received, to determine a pavement condition for the road segment
using the precipitation type and the pavement temperature, and in
response to determining that the pavement temperature is not
received, to determine the pavement condition for the road segment
using the precipitation type, wherein determining the road hazard
condition for the road segment further includes using the pavement
condition.
17. The system of claim 14, further comprising: a visibility level
module to determine a visibility level for the road segment using
the precipitation type, wherein determining the road hazard
condition for the road segment further includes using the
visibility level.
18. The system of claim 17, wherein the visibility level module is
further configured to receive a wind speed, and wherein determining
the visibility level for the road segment further includes using
the wind speed.
19. The system of claim 14, further comprising: a display module to
display the road hazard condition for the road segment.
20. A system for assessing a road hazard condition for a road
segment, the system comprising: a pavement condition module to
receive a pavement temperature, to determine a pavement condition
based on the pavement temperature, and to determine the road hazard
condition for the road segment using the pavement condition.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application claims priority from U.S. Provisional
Patent Application No. 61/893,653, filed Oct. 21, 2013, entitled
"Road Weather Hazard System," the contents of which are
incorporated herein by reference.
TECHNICAL FIELD
[0003] The present application relates to driving information
systems, and more particularly, to a road and weather hazard
system.
BACKGROUND OF THE APPLICATION
[0004] Adverse weather conditions have a major impact on the safety
and operation of roads, from signalized arterials to Interstate
highways. Weather affects driver behavior, vehicle performance,
pavement friction, and roadway infrastructure. Weather events and
their impacts on roads can be viewed as predictable, non-recurring
incidents that affect safety, mobility and productivity. Weather
affects roadway safety through increased crash risk, as well as
exposure to weather-related hazards. Weather impacts roadway
mobility by increasing travel time delay, reducing traffic volumes
and speeds, increasing speed variance, and decreasing roadway
capacity. Weather events influence productivity by disrupting
access to road networks, and increasing road operating and
maintenance costs.
[0005] Previous systems that provide current travel and road
information to travelers include state 511 sites. Road-specific
data that are presented on the 511 site are typically submitted by
maintenance worker's reports of conditions experienced. The 511
site data are generally only applicable for wide stretches of
roadway, and are frequently multiple hours old.
[0006] Other prior road hazard warning systems require mobile data
to function and fail to take full advantage of ancillary
information available such as dual-polarization radar, which can
detect precipitation type, the Naval Research Laboratory cloud
classification satellite data, weather station observations, ground
cover information, and precipitation history. Without the use of
this additional ancillary input data, it is not possible to produce
high quality, physically-relevant inferences of weather conditions
along the roadway.
[0007] What is needed is an increasingly accurate, reliable and
precise system for assessing and communicating weather and road
hazard information to travelers that integrates more of the
available data sources.
SUMMARY OF THE APPLICATION
[0008] A method for assessing a road hazard condition is provided
according to an embodiment. The method includes the step of
receiving remote weather data. The method further includes the step
of determining a precipitation type for a road segment using the
remote weather data. The method further includes the step of
determining a road hazard condition for the road segment using the
precipitation type.
[0009] A system for assessing a road hazard condition is provided
according to an embodiment. The system includes a precipitation
type module to receive remote weather data for the road segment, to
determine a precipitation type for the road segment using the
remote weather data, and to determine a road hazard condition for
the road segment using the precipitation type
[0010] A system for assessing a road hazard condition for a road
segment is provided according to an embodiment of the application.
The system includes a pavement condition module to receive a
pavement temperature, to determine a pavement condition based on
the pavement temperature, and to determine the road hazard
condition for the road segment using the pavement condition.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011] FIG. 1 depicts a road hazard system 100, in accordance with
an embodiment of the application.
[0012] FIG. 2 depicts a method 200 for assessing road conditions,
in accordance with an embodiment of the application.
[0013] FIG. 3 depicts a method 300 for assessing road conditions,
in accordance with an embodiment of the application.
[0014] FIG. 4 depicts a method 400 for assessing road conditions,
in accordance with an embodiment of the application.
[0015] FIG. 5 depicts a method 500 for assessing road conditions,
in accordance with an embodiment of the application.
[0016] FIG. 6 depicts a method 600 for assessing road conditions,
in accordance with an embodiment of the application.
[0017] FIG. 7 depicts a method 700 for assessing road conditions,
in accordance with an embodiment of the application.
[0018] FIG. 8 depicts a method 800 for assessing road conditions,
in accordance with an embodiment of the application.
[0019] FIG. 9 depicts a block diagram of an example computer system
900 in which embodiments of the present application may be
implemented.
DETAILED DESCRIPTION OF THE APPLICATION
[0020] FIGS. 1-9 and the following description depict specific
examples to teach those skilled in the art how to make and use the
best mode of the application. For the purpose of teaching inventive
principles, some conventional aspects have been simplified or
omitted. Those skilled in the art will appreciate variations from
these examples that fall within the scope of the application. Those
skilled in the art will appreciate that the features described
below may be combined in various ways to form multiple variations
of the application. As a result, the application is not limited to
the specific examples described below, but only by the claims and
their equivalents.
[0021] FIG. 1 depicts a road hazard system 100, in accordance with
an embodiment of the application. System 100 includes a
precipitation type module 110, a pavement condition module 120, a
visibility level module 130, and a road hazard module 140. System
100 may include one, or fewer than all of precipitation type module
110, Pavement condition module 120, and visibility level module
130. In embodiments, system 100 may include road hazard module 140
with any of the above-mentioned combinations. This application
further contemplates further modules may be included that are not
depicted in system 100.
[0022] As may be seen in FIG. 1, precipitation type module 110 may
generate precipitation type 118 using remote weather data 112,
first mobile data 114, and/or second mobile data 116, as depicted
with solid connector lines. Precipitation type module 110 may also
generate precipitation type confidence level 119 using remote
weather data 112, first mobile data 114, second mobile data 116,
and/or precipitation type 118, as depicted with broken connector
lines.
[0023] As may be further seen in FIG. 1, pavement condition module
120 may generate pavement condition 126, using precipitation type
118, a pavement temperature 122, and/or vehicle drive information
124, as depicted with solid connector lines. Pavement condition
module 120 may further generate slickness flag 128, and pavement
condition output 129 using precipitation type 118, vehicle drive
information 124, and/or pavement condition 126, as depicted with
solid connector lines. Pavement condition module 120 may also
generate pavement condition confidence level 127 using
precipitation type 118, a pavement temperature 122, vehicle drive
information 124, and/or slickness flag 128, as depicted with broken
connector lines
[0024] As may be further seen in FIG. 1, visibility level module
130 may generate visibility level 138 using precipitation type 118,
wind speed 132, visibility information 134, and/or automobile
operation information 136, as depicted with solid connector lines.
Road hazard module 140 may determine a road hazard condition 142
using precipitation type 118, pavement condition 126, slickness
flag 128, pavement condition output 129, and/or visibility level
138, as depicted with solid connector lines. Visibility level
module 130 may also generate visibility confidence level 139 using
wind speed 132, visibility information 134, automobile operation
information 136, and/or precipitation type confidence level 119, as
depicted with broken connector lines.
[0025] A detailed discussion of each of precipitation type module
110, pavement condition module 120, visibility level module 130,
and road hazard module 140 is provided in the description
below.
[0026] FIG. 2 depicts a method 200 for assessing road conditions,
in accordance with an embodiment. Method 200 is an example
embodiment of precipitation type module 110 of system 100.
[0027] Method 200 begins with step 202. In step 202, remote weather
data is received. For example, remote weather data 112 may be
received. Remote weather data 112 includes data that is received
via ancillary sources that may typically be used for weather
observations. Remote weather data 112 may include, but is not
limited to: radar data, satellite cloud data, weather station air
temperature data. Radar data may include any type of radar
typically used in weather observations, for example
dual-polarization radar data. Satellite cloud classification data
may include any type of satellite data commonly used in weather
observations, for example the Naval Research Laboratory cloud
classification satellite data. Weather station air temperature data
may be received from any type of surface or in situ weather
station. For example weather station air temperature data may be
received from the Rapid Update Cycle Surface Assimilation System
(RSAS). In further embodiments, remote weather data 112 received by
precipitation type module 110 may include other ancillary weather
observation data.
[0028] Method 200 continues with step 204. In step 204, a
precipitation type is determined for a road segment using the
remote weather data. For example, precipitation type 118 may be
determined using remote weather data 112. A road segment is any
portion of a road for which a road or weather hazard may be
identified. For example, a road segment may be a one mile long
segment of a road. In an embodiment, based on remote weather data
112, precipitation type 118 may be determined to be: `no
precipitation`, `precipitation`, `snow`, `mix`, `rain`, `light
precipitation`, `moderate precipitation`, `heavy precipitation`,
`light snow`, `moderate snow`, `heavy snow`, `light mix`, `moderate
mix`, `heavy mix`, `light rain`, `moderate rain`, `heavy rain`, or
`road splash`, in addition to other precipitation types. The type
`precipitation` is a catch-all that may include any type of
precipitation. The precipitation type `mix` may include a mix of
`snow` and `rain`.
[0029] In an example embodiment of precipitation type module 110,
there may be five combinations of remote weather data 112 that may
be received and used to make a first level determination of
precipitation type. The precipitation type 118 determined may
depend upon the types of remote weather data 112 received and/or
the values of the remote weather data 112 received.
[0030] In a first case of a first level of determining
precipitation type 118, remote weather data 112 may include only
radar data. The radar data may include dual-polarization radar
data. If polarimetric radar data is received, the hydrometeor
identification may be used to determine the precipitation types
`snow`, `rain`, or `no precipitation` if there is no meteorological
return. For the precipitation type `snow` or `rain`, the horizontal
reflectivity may be used to further determine precipitation
intensity. For example, if the hydrometeor data identifies the
precipitation type `snow`, a horizontal reflectivity of less than
10 dBZ may determine `light snow`, over 20 dBZ may determine `heavy
snow`, and between 10 and 20 dBZ may determine `moderate snow`. If
the hydrometeor data identifies the precipitation type `rain`, a
horizontal reflectivity of less than 20 dBZ may determine `light
rain`, over 40 dBZ may determine `heavy rain`, and between 20 and
40 dBZ may determine `moderate rain`.
[0031] If the radar data does not include polarimetric radar data,
however, then snow may not be distinguishable from rain. A general
precipitation intensity type may still be determined, however. For
example, `no precipitation` may be determined for a horizontal
reflectivity of less than -30 dBZ, between -30 and 15 dBZ may
determine `light precipitation`, between 15 to 40 dBZ may determine
`moderate precipitation`, and over 40 dBZ may determine `heavy
precipitation`.
[0032] In a second case of a first level of determining
precipitation type 118, remote weather data 112 may include
satellite cloud classification data. For example, NRL cloud
classification data may be received to determine the types
`precipitation` or `no precipitation`.
[0033] In a third case of a first level of determining
precipitation type 118, remote weather data 112 may include radar
and weather station air temperature data. [0034] If polarimetric
radar data is received, the hydrometeor identification may be used
to determine the precipitation types `snow`, `rain`, or `no
precipitation`. If the precipitation type `snow` or `rain` is
determined, the precipitation phase may be checked against the
weather station air temperature data. If the weather station air
temperature data is less than -5.degree. C., the precipitation type
118 may be changed to `snow`. If the weather station air
temperature data is greater than 5.degree. C., the precipitation
type 118 may be changed to `rain`. If the weather station air
temperature data is between -5.degree. C. and 5.degree. C., the
precipitation type 118 may remain unchanged. Radar horizontal
reflectivity may be further used to determine the intensity of the
precipitation type 118. For example, if a precipitation type `snow`
is determined, a horizontal reflectivity of less than 10 dBZ may
determine the precipitation type `light snow`, greater than 20 dBZ
may determine `heavy snow`, and between 10 and 20 dBZ may determine
`moderate snow`. If the precipitation type `rain` is determined,
however, a horizontal reflectivity of less than 20 dBZ may
determine the precipitation type `light rain`, greater than 40 dBZ
may determine `heavy rain`, and between 20 and 40 dBZ may determine
`moderate rain`. [0035] If polarimetric data is not available, then
the weather station air temperature may be used to determine the
precipitation type `snow` for a temperature of less than -2.degree.
C., `rain` for a temperature that is greater than 2.degree. C., and
`mixed` if the temperature is between -2 and 2.degree. C. For the
precipitation type `snow`, a horizontal radar reflectivity of less
than 10 dBZ may determine the precipitation type `light snow`, over
20 dBZ may determine `heavy snow`, and between 10 and 20 dBZ may
determine `moderate snow`. For the precipitation types `rain` or
`mixed`, `light rain` or `light mixed` may be determined for a
radar horizontal reflectivity of than 20 dBZ, and the precipitation
types `heavy rain` or heavy mixed' for a horizontal reflectivity
greater than 40 dBZ, and the precipitation types `moderate rain` or
`moderate mixed` for a horizontal reflectivity between 20 and 40
dBZ.
[0036] In a fourth case of a first level of determining
precipitation type 118, remote weather data 112 may include
satellite cloud classification and weather station air temperature
data. The satellite cloud classification may be used to determine
the precipitation types `precipitation` and `no precipitation`. If
the type `precipitation` is determined, the precipitation type 118
will be changed to `snow if the weather station air temperature is
less than -2.degree. C., to `rain` if the temperature is greater
than 2.degree. C., and to `mixed` if the temperature is between
-2.degree. C. and 2.degree. C.
[0037] In a fifth case of a first level of determining
precipitation type 118, remote weather data 112 may include radar
data, satellite cloud classification data, and weather station air
temperature data. [0038] If polarimetric radar data is received,
the hydrometeor identification may be used to determine a
precipitation type of `snow`, `rain`, or `no precipitation`. If the
precipitation type `snow` or `rain` is determined, the
precipitation phase may be checked against the weather station air
temperature data. If the weather station air temperature data is
less than -5.degree. C., the precipitation type 118 may be changed
to `snow`. If the weather station air temperature data is greater
than 5.degree. C., the precipitation type 118 may be changed to
`rain`. If the weather station air temperature data is between
-5.degree. C. and 5.degree. C., the precipitation type 118 may not
be changed, however. Radar horizontal reflectivity may be further
used to determine the intensity of the precipitation type 118. For
example, if a precipitation type `snow` is determined, a horizontal
reflectivity of less than 10 dBZ may determine the precipitation
type `light snow`, greater than 20 dBZ may determine `heavy snow`,
and between 10 and 20 dBZ may determine `moderate snow`. If the
precipitation type `rain` is determined, however, a horizontal
reflectivity of less than 20 dBZ may determine the precipitation
type `light rain`, greater than 40 dBZ may determine `heavy rain`,
and between 20 and 40 dBZ may determine `moderate rain`. [0039] If
no polarimetric data is available, then the weather station air
temperature may be used to determine the precipitation type `snow`
for a temperature of less than -2.degree. C., `rain` for a
temperature that is greater than 2.degree. C., and `mixed` if the
temperature is between -2 and 2.degree. C. For the precipitation
type `snow`, a horizontal radar reflectivity of less than 10 dBZ
may determine the precipitation type `light snow`, over 20 dBZ may
determine `heavy snow`, and between 10 and 20 dBZ may determine
`moderate snow`. For the precipitation types `rain` or `mixed`,
`light rain` or `light mixed` may be determined for a radar
horizontal reflectivity of less than 20 dBZ, and the precipitation
types `heavy rain` or heavy mixed' for a horizontal reflectivity
greater than 40 dBZ, and the precipitation types `moderate rain` or
`moderate mixed` for a horizontal reflectivity between 20 and 40
dBZ. In embodiments, the resulting precipitation type category may
be compared to satellite cloud classification data and modified
accordingly.
[0040] In embodiments, step 204 may further include determining a
precipitation type confidence level 119. A confidence level
reflects the amount of trust that may be placed in a condition
determined in general, and a precipitation type confidence level
119 specifically reflects the trust that may be placed in the
determination of precipitation type 118 by the precipitation type
module 110 for a segment of road. The precipitation type confidence
level 119 may be `low`, `medium`, or `high`. In an example
implementation, the precipitation type confidence level 119 may be
determined to be `medium` if remote weather data 112 includes radar
data, and `low` if remote weather data 112 does not include radar
data. In embodiments, the precipitation type confidence level 119
may be used to further determine a road hazard condition 142.
[0041] Step 204 provides an initial precipitation type inference
using only ancillary, traditional weather observation data. The
precipitation type 118 determined in step 204 may be further
determined based upon available mobile data, as described
below.
[0042] Method 200 continues with step 206. In step 206, a road
hazard condition for the road segment is determined using the
precipitation type. For example, road hazard condition 142 may be
determined using precipitation type 118. Road hazard condition 142
is a message, notification, or alert regarding a driving condition
directed to an end user, such as a driver. In embodiments, road
hazard condition 142 may include information identifying
precipitation type 118, in addition to further information, as
described below.
[0043] In embodiments, step 204 of method 200 may be performed with
additional steps. For example, additional levels of determining
precipitation type 118 may incorporate mobile data. Mobile data
includes any data received from a mobile source. For example, FIG.
3 depicts method 300. Method 300 begins with step 302, which is
performed with, or immediately following step 204. In step 302, a
first mobile data is received.
[0044] For example, precipitation type module 110 may receive first
mobile data 114. First mobile data 114 may include, but is not
limited to a wiper status and a mobile air data. A wiper status is
any status that may include information about whether a vehicle
windshield wiper is operating and the speed of operation. In an
example embodiment, the wiper status may include the states `off`,
`intermittent`, `low`, or `high`. A mobile air data is a
vehicle-measured ambient air temperature that may be determined
using any type of temperature monitoring equipment known to those
of skill in the art.
[0045] Method 300 continues with step 304. In step 304, a
precipitation type is further determined for a road segment using
the first mobile data. For example, precipitation type module 110
may further determine the precipitation type 118 using the first
mobile data 114. In embodiments, the precipitation type confidence
level 119 for the road segment may be further determined using the
first mobile data 114.
[0046] In a first case of a second level of determining
precipitation type 118, the first mobile data 114 may include a
mobile air data and remote weather data 112 may include a weather
station temperature. The mobile air data may be compared to the
weather station air temperature data, and the precipitation type
118 may be further determined in one of the three following ways:
[0047] If the absolute value of the difference between the vehicle
and weather station air temperatures has an absolute value that is
less than 1.degree. C., no further determination of the
precipitation type 118 is made at the second level. [0048] If the
difference between the vehicle and weather station air temperatures
has an absolute value that is greater than 1.degree. C. and remote
weather data 112 includes polarimetric radar data, the
precipitation type 118 may be further determined to be `snow` if
the mobile air data is less than -5.degree. C., `rain` if greater
than 5.degree. C., and not changed if between -5 and 5.degree. C.
[0049] If the absolute value of the difference between the vehicle
and weather station air temperatures is greater than 1.degree. C.
and remote weather data 112 does not include polarimetric radar
data, the precipitation type 118 may be changed to `snow` if the
mobile air data is less than -2.degree. C., `rain` if the mobile
air data is greater than 2.degree. C., and `mixed` if the mobile
air data is between -2 and 2.degree. C.
[0050] In a second case of a second level of determining
precipitation type 118, the first mobile data 114 may include a
mobile air data and remote weather data 112 may not include weather
station temperature data. The precipitation type 118 may be further
determined in one of the three following ways: [0051] If remote
weather data 112 includes polarimetric radar data, the
precipitation type 118 may be further determined using the mobile
air data. For example, if the mobile air data is less than
-5.degree. C. the precipitation type 118 may be determined to be
`snow`, if the mobile air data is greater than 5.degree. C. the
precipitation type 118 may be determined to be `rain`, and the
precipitation type 118 may not be changed if the mobile air data is
between -5 and 5.degree. C. If the precipitation type 118 is
determined to be `snow`, then the precipitation type 118 may be
further determined to be `light snow` if the radar horizontal
reflectivity is less than 10 dBZ, `heavy snow` if greater than 20
dBZ, and `moderate snow` if between 10 and 20 dBZ. If the
precipitation type 118 is determined to be `rain`, then the
precipitation type 118 may be further determined to be `light rain`
if the reflectivity is less than 20 dBZ, `heavy rain` if greater
than 40 dBZ, and `moderate rain` if 20 to 40 dBZ. If remote weather
data 112 includes satellite cloud classification data, the
precipitation type 118 may be further determined based upon the
satellite cloud classification data. [0052] If remote weather data
112 includes no polarimetric radar data or weather station air
temperature data, but does include horizontal reflectivity radar
data, the precipitation type 118 may be determined to be `snow` if
the mobile air data is less than -2.degree. C., `rain` if the
mobile air data is greater than 2.degree. C., and `mixed` if the
mobile air data is -2 to 2.degree. C. If the precipitation type 118
is determined to be `snow`, the precipitation type 118 may be
further determined to be `light snow` if the radar horizontal
reflectivity is less than 10 dBZ, `heavy snow` if greater than 20
dBZ, and `moderate snow` if 10 to 20 dBZ. If the precipitation type
118 is determined to be `mixed` or `rain`, the precipitation type
118 may be further determined to be `light mixed` or `light rain if
the reflectivity is less than 20 dBZ, `heavy mixed` or `heavy snow`
if greater than 40 dBZ, and `moderate mixed` or `moderate snow` if
20 to 40 dBZ. If remote weather data 112 includes satellite cloud
classification data, the precipitation type 118 may be further
determined based upon the satellite cloud classification data.
[0053] In further embodiment of the second level of determining
precipitation type 118, the first mobile data 114 may include wiper
status. If the first mobile data 114 includes the wiper status, the
precipitation type 118 may be further determined made based on the
following wiper status values: [0054] If wiper status is `off`:
[0055] `no precipitation`, `light rain` or `light snow` are
unchanged, [0056] `moderate rain` and `heavy rain` changed to
`light rain`, [0057] `moderate snow` changed to `light snow`,
[0058] `heavy snow` is changed to `moderate snow`, [0059] `rain`
changed to `light rain`, [0060] `snow` changed to `light snow`,
[0061] `mixed` changed to `light mixed`, [0062] `precipitation`
changed to `light precipitation`. [0063] If wiper status is
`intermittent`: [0064] `light` or `moderate` precipitation of any
type are unchanged, [0065] `no precipitation` is changed to `road
splash` [0066] `heavy` precipitation of any type is changed to
`moderate` [0067] `rain` changed to `light rain`, [0068] `snow`
changed to `light snow`, [0069] `mixed` changed to `light mixed`,
and [0070] `precipitation` changed to `light precipitation. [0071]
If wiper status is `low`: [0072] `moderate` or `heavy`
precipitation of any type are unchanged, [0073] `no precipitation`
is changed to `road splash`, [0074] `light` precipitation of any
type is changed to `moderate`, [0075] `rain` changed to `moderate
rain`, [0076] `snow` changed to `moderate snow`, [0077] `mixed`
changed to `moderate mixed`, and [0078] `precipitation` changed to
`moderate precipitation. [0079] If wiper status is `high`: [0080]
`moderate rain`, `heavy rain` and `heavy snow` are unchanged,
[0081] `no precipitation` is changed to `road splash`, [0082]
`light` precipitation of any type is changed to `moderate`, [0083]
`moderate snow` is changed to `heavy snow` [0084] `rain` changed to
`heavy rain`, [0085] `snow` changed to `heavy snow`, [0086] `mixed`
changed to `heavy mixed`, and [0087] `precipitation` changed to
`heavy precipitation.
[0088] In an embodiment, the precipitation type confidence level
119 may be further determined at the second level based on the
first mobile data 114: [0089] The precipitation type confidence
level 119 may be changed to be `low` if the precipitation type 118
is `no precipitation`, `rain`, `snow`, or `mixed`. [0090] The
precipitation type confidence level 119 may be changed to `medium`
if the first mobile data 114 fails to include a wiper status or a
mobile air data. [0091] The precipitation type confidence level may
be changed to `high` if the precipitation type 118 includes a type
('rain', `snow`, or `mixed`) and an intensity (light', `moderate`,
or `heavy`) and the first mobile data 114 includes both a wiper
status and a mobile air data.
[0092] Method 300 continues with step 305. In step 305, it is
determined whether the precipitation type 118 will be further
determined using second mobile data. If it is determined that
precipitation type will be further determined using second mobile
data 116, method 300 may continue to step 306. If it is determined
that precipitation type will not be further determined using second
mobile data 116, however, then method 300 may end and method 200
may continue with step 206.
[0093] In embodiments, steps 306 and 308 may represent a third
level of determining precipitation type 118. In step 306, a second
mobile data is received. For example, second mobile data 116 may
include, but is not limited to, at least one of a speed ratio and a
headlight status. A speed ratio may be determined by calculating
the ratio of the vehicle speeds on the segment to the posted speed
limit for that segment. A headlight status may include an indicator
of whether the headlights are `off` or `on`.
[0094] In step 308, the precipitation type 118 is further
determined using the second mobile data 116.
[0095] In a first case of a third level of determining
precipitation type 118, the second mobile data 116 may include a
speed ratio. If speed ratio is present, then the precipitation type
118 may be further determined as follows: [0096] If the speed ratio
is greater than 0.7, the precipitation type 118 `heavy snow` is
changed to `moderate snow`, [0097] If the speed ratio is greater
than 0.8, the precipitation type 118 `heavy rain` is changed to
`moderate rain`.
[0098] In a second case of a third level of determining
precipitation type 118, the second mobile data 116 may include a
headlight status. If headlight status is present, then the
precipitation type 118 may be further determined as follows: [0099]
If the current time falls into a nighttime range, no precipitation
type 118 change is made. [0100] If the headlight status is `off`, a
`moderate` precipitation type 118 is changed to a `light`
precipitation type 118 and a `heavy` precipitation is changed to a
`moderate` precipitation type 118.
[0101] In an embodiment, the precipitation type confidence level
119 may be further determined at the third level based on the
further determination of precipitation type using second mobile
data 116. For example, the precipitation type confidence level 119
at a third level may be determined to be `high`.
[0102] After step 308 has been performed, method 300 may conclude,
and method 200 may continue with step 206, as described above.
[0103] FIG. 4 depicts an example embodiment of pavement condition
module 120 in accordance with an embodiment. Method 400 begins with
step 402. In step 402, it is determined whether a pavement
temperature has been received. For example, pavement condition
module 120 may receive pavement temperature 122. Pavement
temperature 122 may be determined via a mobile source or via a
surface weather station, etc.
[0104] If pavement temperature 122 is received in step 402, method
400 continues with step 404. In step 404, a pavement condition is
determined using the precipitation type and the pavement
temperature. A pavement condition describes the condition of a road
segment. In embodiments, a pavement condition may be determined to
be `dry`, `snow`, `ice`, `wet`, `dry/snow/ice`, or `dry/wet`. In an
example embodiment, the pavement condition may be determined in
step 404 as follows: [0105] If the pavement temperature is less
than -2.degree. C.: [0106] if precipitation type 118 is `no
precipitation`, the pavement condition is determined to be `dry`,
[0107] if the precipitation type 118 is `precipitation`, `snow`, or
`road splash`, the pavement condition is determined to be `snow`,
and [0108] if the precipitation type 118 is `mixed`, `rain`, the
pavement condition is determined to be `ice`. [0109] If the
pavement temperature is greater than -2.degree. C.: [0110] if the
precipitation type 118 is `no precipitation`, the pavement
condition is determined to be `dry`, and [0111] if the
precipitation type 118 is any type besides `no precipitation`, the
pavement condition is determined to be `wet`.
[0112] If pavement temperature 122 is not received in step 402,
method 400 continues with step 406. In step 406, a pavement
condition is determined using the precipitation type 118 as
follows: [0113] If the precipitation type 118 is `no precipitation`
the pavement condition is determined to be `dry`. [0114] If the
precipitation type 118 is `rain` or `road splash`, the pavement
condition is determined to be to `wet`. [0115] If the precipitation
type 118 is `snow` or `mixed`, the pavement condition is determined
to be `snow`.
[0116] Method 400 continues after steps 404 or 406 with step 408.
In step 408, the road hazard condition for the road segment is
further determined using pavement condition 126.
[0117] FIG. 5 depicts a further example embodiment of pavement
condition module 120. Method 500 is similar to method 400, except
method 500 determines a pavement condition and/or a road hazard
condition without using precipitation type 118.
[0118] Method 500 begins with step 502. In step 502, a pavement
temperature is received.
[0119] Method 500 continues with step 504. In step 504, a pavement
condition is determined using the pavement temperature. In an
example embodiment, the pavement condition 126 may be determined in
step 504 as follows: [0120] If the pavement temperature is less
than -2.degree. C., the pavement condition is determined to be
`dry/snow/ice`. [0121] If the pavement temperature is determined to
be greater than -2.degree. C., the pavement condition is determined
to be `dry/wet`.
[0122] Method 500 continues with step 506. In step 506, the road
hazard condition for the road segment is further determined using
pavement condition 126.
[0123] In embodiments, methods 400 or 500 may include steps
additional to, or immediately following any of steps 404, 406, or
504. For example, FIG. 6 depicts method 600. Method 600 begins with
step 602. In step 602, vehicle drive information is received.
Vehicle drive information may include, but is not limited to: an
automatic brake system (ABS) status, a traction status or a
stability control observation, or a yaw rate. The traction status
and the stability control observation indicate whether a vehicle is
`engaged` or `not engaged`.
[0124] Method 600 continues with step 604. In step 604, a slickness
flag is determined using the vehicle drive information. A slickness
flag indicates that pavement conditions are slick, or that traction
may otherwise be diminished for a road segment. In an example
embodiment, the slickness interest level, slick_int, may be
determined. An interest value is a value between 0 and 1 in fuzzy
logic that represents the possibility that a respective condition
is present. For example the possibility of slick pavement
conditions, slick_int, may be estimated using on the following
fuzzy logic:
slick_int=0.3*p+0.3*r+0.2*s+0.1*i+0.1*d (Eqn 1)
where:
p = { - 1 if precipitation type is no precipitation - 0.5 if
precipitation type is light rain , moderate rain , or road splash 0
if precipitation type is heavy rain 0.5 if precipitation type is
light snow , moderate snow , mixed 1 if precipitation type is heavy
snow r = { - 1 if pavement condition is dry pavement 0 if pavement
condition is wet or wet / dry pavement 0.5 if pavement condition is
dry / snow / ice pavement 1 if pavement condition is snow / ice s =
{ 0 if ABS , traction control , and stability control all not
activated 1 if ABS , traction control , or stability control is
activated i = { IQR ( yaw rate ) if IQR ( yaw rate ) .ltoreq. 1 1
if IQR ( yaw rate ) > 1 d = { max ( yaw rate - median ( yaw rate
) if max ( yaw rate - median ( yaw rate ) .ltoreq. 1 1 if max ( yaw
rate - median ( yaw rate ) > 1 ##EQU00001##
If slick_int is greater than or equal to 0.44, then the slickness
flag 128 is set to true. In further embodiments, the slickness flag
128 may be included with a pavement condition 126 based on whether
the pavement condition 126 is determined to be `wet`, `snow`, `ice`
or any combination thereof.
[0125] Method 600 continues with step 606. In step 606, a pavement
condition output is determined using the pavement condition and the
slickness flag. The pavement condition output 129 may be used to
indicate the condition of the road segment in a user-friendly
format that incorporates both pavement condition 126 and slickness
flag 128. In an example embodiment, the pavement condition output
129 may be determined based on the following rules: [0126] if
pavement condition is `dry/wet`, pavement condition output is `not
icy`, [0127] if pavement condition is `dry/wet` and slickness flag,
pavement condition output is `wet` [0128] if pavement condition is
`dry/snow/ice`, pavement condition output is `ice possible` [0129]
if pavement condition is `dry/snow/ice` and slickness flag,
pavement condition output is `icy` [0130] if pavement condition is
`dry`, pavement condition output is `dry` [0131] if pavement
condition is `wet`, pavement condition output is `wet` [0132] if
pavement condition is `wet` and slickness flag, pavement condition
output is `wet, hydroplaning possible` [0133] if pavement condition
is `snow`, pavement condition output is `snow` [0134] if pavement
condition is `snow` and slickness flag, pavement condition output
is `slick, snowy` [0135] if pavement condition is `ice`, pavement
condition output is `icy` [0136] if pavement condition is `ice` and
slickness flag, pavement condition output is `slick, icy` The rules
for determining pavement condition output 129 described above are
not intended to be limiting. Other rules and inputs are also
possible, as will be understood by those who are skilled in the
art.
[0137] In embodiments, methods 400, 500, or 600 may further include
determining a pavement condition confidence level 127 using
pavement temperature 122 and the precipitation type 118. A pavement
condition confidence level 127 reflects the level of trust that may
be placed in any combination of a pavement condition 126, a
slickness flag 128, or a pavement condition output 129. In an
example embodiment, the pavement condition confidence level 127 may
be determined based on the following rules: [0138] if a pavement
temperature is received but no precipitation type 118 is available,
or the precipitation type 118 confidence level is `low`, the
pavement condition confidence level 127 is `low` [0139] if no
pavement temperature is received but precipitation type 118 is
available, or the precipitation type 118 confidence level is
`medium, the pavement condition confidence level 127 is `medium`
[0140] if a pavement temperature is received and precipitation type
118 is available, the pavement condition confidence level 127 is
`high [0141] if there is a slickness flag and the vehicle drive
information does not include automatic brake system status,
traction status, or stability control status, the pavement
condition level is set to `medium`. The rules for determining the
pavement condition confidence level 127 described above are not
intended to be limiting. Other rules and inputs are also possible,
as will be understood by those of skill in the art.
[0142] FIG. 7 depicts method 700, an example embodiment of a
visibility level module 130. Method 700 begins with step 702. In
step 702, a visibility level for a road segment is determined using
the precipitation type. A visibility level describes the clarity
with which a motorist may expect to see through the outside air
while operating a vehicle on the road segment.
[0143] In an example embodiment, if the precipitation type 118 is
`heavy rain`, with a `medium` or `high` precipitation type
confidence level 119, the visibility level 138 may be determined to
be `heavy rain`. If the precipitation type 118 is `heavy snow, with
a `medium` or `high` precipitation type 118 confidence level, the
visibility level 138 may be determined to be `heavy snow.
[0144] Method 700 continues with step 704. In step 704, the road
hazard condition for the road segment is further determined using
the visibility level 138.
[0145] In embodiments, method 700 may include steps additional to,
or immediately following step 702 to further determine the
visibility level. For example, FIG. 8 depicts method 800. Method
800 begins with step 801. In step 801, it is determined whether
visibility level will be further determined using a wind speed. If
visibility level will be further determined using a wind speed,
step 801 continues with step 802. If visibility level will not be
further determined using a wind speed, however, step 801 continues
with step 805.
[0146] If a wind speed is determined to be available in step 801,
method 800 continues with step 802. In step 802, a wind speed is
received. In embodiments, the wind speed may be received from any
type of weather instrument commonly known to those of skill in the
art, including a mobile, surface, or remote weather instrument.
[0147] Method 800 continues with step 804. In step 804, the
visibility level is further determined using the wind speed. In an
example embodiment, if the wind speed 132 is over a threshold level
and the precipitation type 118 is any intensity of `snow`, the
visibility level 138 may be determined to be `blowing snow`. If the
wind speed 132 is over a threshold level and the pavement condition
includes `snow`, the visibility level 138 may also be determined to
be `blowing snow`.
[0148] Method 800 continues with step 805. If visibility level will
be further determined using visibility information, step 805
continues with step 806. If visibility level will not be further
determined using visibility information, step 805 continues with
step 809. In embodiments, visibility level will only be further
determined using visibility information if visibility level 138
does not include `blowing snow`, `heavy snow` or `heavy rain` after
step 804. In other embodiments, visibility level will only be
further determined using visibility information regardless of the
visibility level 138 determined in step 804, however.
[0149] Method 800 continues with step 806. In step 806, visibility
information is received. For example, visibility level module 130
may receive visibility information 134. Visibility information 134
includes information or data that may be used to determine the
visibility conditions on a road segment. For example, the
visibility information 134 may include, but is not limited to: a
relative humidity, a percentage of fog lights on, a percentage of
high beams on, a speed ratio, a station visibility, a
station-reported visibility type, a wildfire existence indicator, a
wind direction, and a dust existence indicator, etc. Relative
humidity may be determined using any type of algorithm and weather
instrument commonly known to those of skill in the art. In an
example embodiment, the relative humidity may be received from a
vehicle information source. If no vehicle humidity information is
available, the relative humidity may be calculated using the mobile
air data and the nearest weather station dewpoint temperature.
Alternatively, if no mobile air data is available, the relative
humidity may be determined using the nearest weather station
relative humidity measurement. The percentage of fog lights on
indicates the percentage of fog lights of the total number of
available fog lights on a vehicle that are powered on. The
percentage of high beams indicates the percentage of high beam
headlights of the total number of available high beam headlights on
a vehicle that are powered on. The station visibility represents a
distance that may be seen from a weather station. The
station-reported visibility type may include `fog`, `haze`, `dust`,
or `smoke`. The wildfire existence indicator determines whether
there is a wildfire within a threshold distance of a road segment.
The dust existence indicator indicates whether dusty areas exist
within a threshold distance of a road segment. In embodiments, the
dust existence indicator may be determined using information about
landscape and historical record of precipitation in an area.
[0150] Method 800 continues with step 808. In step 808, the
visibility level is further determined using visibility information
134. In an embodiment, visibility level module 130 may determine
whether a visibility hazard that includes fog, haze, smoke, and
dust, in addition to other possible visibility hazards, may further
determine the visibility level 138. For example, a visibility
hazard may be determined using fuzzy logic with the following
equation:
hazard=max(fog_int, haze_int, smoke_int, dust_int) (Eqn 2)
[0151] if hazard >0.4, output hazard
where fog_int is an interest value for fog, haze_int is an interest
value for haze, smoke_int is an interest value for smoke, and
dust_int is an interest value for dust. The maximum interest value
for each of fog_int, haze_int, smoke_int, and dust_int is returned
by Equation 2. If the maximum interest value is greater than 0.4,
then a further visibility hazard has been identified. If all
interest values are less than or equal to 0.4, then no visibility
hazard is identified. The interest value for each visibility hazard
type may be determined as described below.
[0152] The fog interest value may be calculated as follows:
fog_int = 0.4 * r + 0.2 * f - 0.2 * h + 0.2 * s + 0.1 * v + 0.1 * t
##EQU00002## where : ##EQU00002.2## Relative humidity = x
##EQU00002.3## r = { - 1 if x < 40 ( x / 20 ) - 3 if 40 .ltoreq.
x .ltoreq. 60 0 if 60 < x .ltoreq. 80 ( x / 20 ) - 20 if 80 <
x .ltoreq. 100 1 if x > 100 Percent of fog lights on = x f = { x
/ 100 if 0 .ltoreq. x .ltoreq. 100 Percent of high beams on = x h =
{ x / 100 if 0 .ltoreq. x .ltoreq. 100 Speed ratio = x s = { 5 x if
x < 0.2 ( - 2 x / 3 ) + 17 / 15 if 0.2 .ltoreq. x .ltoreq. 0.5 (
- 8 x / 5 ) + 8 / 5 if 0.5 < x .ltoreq. 1 0 if x > 1 Station
visability = x v = { ( - x / 10 ) + 1 if 0 .ltoreq. x .ltoreq. 10 0
if x > 10 Station - reported visibility type = x t = { 1 if x =
fog 0 if x .noteq. fog ##EQU00002.4##
[0153] The haze interest value may be calculated as follows:
haze_int = 0.6 * r + 0.2 * v + 0.2 * t ##EQU00003## where :
##EQU00003.2## Relative humidity = x ##EQU00003.3## r = { ( x / 40
) - 1 if x < 40 0 if 40 .ltoreq. x .ltoreq. 60 ( x / 20 ) - 3 if
60 < x .ltoreq. 80 ( - x / 20 ) + 5 if 80 < x .ltoreq. 100 0
if x > 100 Station visability = x v = { ( x / 5 ) if 0 .ltoreq.
x .ltoreq. 5 ( - x / 5 ) + 2 if 5 < x .ltoreq. 10 0 if x > 10
Station - reported visibility type = x t = { 1 if x = haze 0 if x
.noteq. haze ##EQU00003.4##
[0154] The smoke interest value may be calculated as follows:
smoke_int = 0.4 * e + 0.3 * w + 0.1 * s + 0.1 * v + 0.1 * t
##EQU00004## where : ##EQU00004.2## Wildfire existence = x
##EQU00004.3## e = { 1 if wildfire exists within n km of road
segment 0 if no wildfire exists within n km of road segment Wind
direction = x w = { 1 if segment is downwind of fire location 0 if
segment is not downwind of fire location Speed ratio = x s = { 5 x
if x < 0.2 ( - 2 x / 3 ) + 17 / 15 if 0.2 .ltoreq. x .ltoreq.
0.5 ( - 8 x / 5 ) + 8 / 5 if 0.5 < x .ltoreq. 1 0 if x > 1
Station visability = x v = { ( - x / 10 ) + 1 if 0 .ltoreq. x
.ltoreq. 10 0 if x > 10 Station - reported visibility type = x t
= { 1 if x = smoke 0 if x .noteq. smoke ##EQU00004.4##
[0155] The dust interest value may be calculated as follows:
dust_int = 0.3 * e + 0.3 * w + 0.2 * s + 0.1 * v + 0.1 * t
##EQU00005## Dust existence = x ##EQU00005.2## e = { 1 if dusty
area exists within n km of road segment 0 if no dusty area exists
within n km of road segment Wind speed ( kph ) = x w = { 0 if x
< 30 ( x / 30 ) - 1 if 30 .ltoreq. x .ltoreq. 60 1 if x > 60
Speed ratio = x s = { 5 x if x < 0.2 ( - 2 x / 3 ) + 17 / 15 if
0.2 .ltoreq. x .ltoreq. 0.5 ( - 8 x / 5 ) + 8 / 5 if 0.5 < x
.ltoreq. 1 0 if x > 1 Station visability = x v = { ( - x / 10 )
+ 1 if 0 .ltoreq. x .ltoreq. 10 0 if x > 10 Station - reported
visibility type = x t = { 1 if x = dust 0 if x .noteq. dust
##EQU00005.3##
If a further visibility hazard is identified, visibility level 138
may further include `fog`, `haze`, `dust`, or `smoke`, as
identified by Equation 2.
[0156] Method 700 and steps 802, 804, 806, and 808 may determine
whether visibility level 138 includes `heavy rain`, `heavy snow`,
`blowing snow`, `fog`, `haze`, `smoke`, or `dust`. Method 800
continues with step 809. In step 809, it is determined whether
visibility level will be further determined using automobile
operation information. If visibility level will be further
determined using automobile operation information, step 809
continues with step 810. If visibility level will not be further
determined using automobile operation information, however, method
800 terminates and method 700 continues with step 704. In
embodiments, visibility level may only be further determined using
automobile operation information if visibility level 138 does not
include `blowing snow`, `heavy snow`, `heavy rain`, `fog`, `haze`,
`smoke`, or `dust` after step 808. In other embodiments, visibility
level may be further determined using automobile operation
information regardless of the visibility level 138, however.
[0157] Method 800 continues with step 810. In step 810, automobile
operation information is received. For example, visibility level
module 130 may receive automobile operation information 136. The
automobile operation information 136 includes information about how
an automobile is being operated on the road segment. For example,
automobile operation information 136 may include, but is not
limited to: a speed ratio, a percentage of lights on, a percentage
of fog lights on, and a percentage of high beams on.
[0158] In step 812, a visibility level is further determined using
the automobile operation information. Specifically, automobile
operation information 136 may be used to determine if a low
visibility hazard may be inferred to further determine visibility
level 138. In an embodiment, fuzzy logic may be applied to
determine a low visibility interest value low_vis using the
following equation:
low_vis = 0.3 * r + 0.25 * s + 0.25 * l + 0.2 * v ##EQU00006##
where : ##EQU00006.2## Relative humidity = x ##EQU00006.3## r = { (
x / 40 ) - 1 if 0 .ltoreq. x < 40 0 if 40 .ltoreq. x .ltoreq. 60
( x / 40 ) - 3 / 2 if 60 < x .ltoreq. 80 1 if x > 100 Speed
ratio = x s = { 5 x if x < 0.2 ( - 2 x / 3 ) + 17 / 15 if 0.2
.ltoreq. x .ltoreq. 0.5 ( - 8 x / 5 ) + 8 / 5 if 0.5 < x
.ltoreq. 1 0 if x > 1 l = 0.375 * o + 0.625 * f - 0.125 * h
where : Percent of lights on = x o = ( x / 100 ) if 0 .ltoreq. x
.ltoreq. 100 Percent of fog lights on = x f = ( x / 100 ) if 0
.ltoreq. x .ltoreq. 100 Percent of high beams on = x h = ( x / 100
) if 0 .ltoreq. x .ltoreq. 100 Station visability = x v = { ( - x /
10 ) + 1 if 0 .ltoreq. x .ltoreq. 10 0 if x > 10
##EQU00006.4##
If the resulting low_vis value is greater than 0.5, then visibility
level 138 may further include low visibility'. Otherwise, if no
visibility hazards have been identified in method 600 or 700, then
the visibility level may be determined to be `normal visibility`.
After method 800 concludes with step 812, method 700 may continue
with step 704.
[0159] In embodiments, a visibility confidence level 139 may be
determined. The visibility confidence level 139 for the road
segment reflects the level of trust that may be placed in the
visibility level 138. The visibility confidence level 139 may be
determined based on the amount of input data provided in
determining the visibility level 138 and the precipitation type 118
confidence level. For example, the visibility confidence level 139
may be determined based upon how many data points were provided,
including the wind speed 132, the visibility information 134, and
the automobile operation information 136, and whether the
precipitation type confidence level 119 was determined to be `low`,
`medium`, or `high`.
[0160] In system 100, it may be seen that road hazard module 140
may determine the road hazard condition 142 based on precipitation
type 118, pavement condition 126, slickness flag 128, pavement
condition output 129, and/or visibility level 138. In an
embodiment, road hazard module 140 may determine road hazard
condition 142 by aggregating the information provided by any
combination of: precipitation type 118, pavement condition 126,
slickness flag 128, pavement condition output 129, and/or
visibility level 138. In a further embodiment, road hazard module
140 may determine road hazard condition 142 via a combined
algorithm test that outputs the worst driving limitation determined
among each of the precipitation type 118, pavement condition 126,
or visibility level 138.
[0161] In embodiments, any of precipitation type module 110,
pavement condition module 120, visibility level module 130, or road
hazard module 140 may be integrated into any end-user type device
to display road hazard information for an end user. For example,
precipitation type module 110, pavement condition module 120,
visibility level module 130, or road hazard module 140 may be
integrated into web services or in-car delivery systems.
Precipitation type module 110, pavement condition module 120,
visibility level module 130, or road hazard module 140 may be
combined with other navigation systems for smart-routing
applications.
[0162] In an embodiment, system 100 or any of methods 200, 300,
400, 500, 600, 700, or 800 may be performed frequently at a high
resolution using the most up to date and objective information
available, providing a more accurate and timely assessment of road
hazards and conditions. For example, any of methods 200, 300, 400,
500, 600, 700, or 800 may be performed every five minutes along
one-mile segments of roadways.
[0163] The system for assessing road conditions described in the
application provides the advantage of combining multiple inputs
from multiple sources to determine road weather hazard conditions
with a high level of certainty. The example logic provided in the
application may determine road hazard conditions using decision
trees and fuzzy logic weights that function to produce complex yet
physically-relevant inferences of weather conditions along the
roadway. Methods 200, 300, 400, 500, 600, 700, or 800 present logic
that has its basis in a physical understanding of atmospheric
processes, in addition to computational intelligence.
[0164] FIG. 9 depicts a block diagram of an example computer system
900 in which embodiments of the present application may be
implemented. The embodiments described herein, including systems,
methods/processes, and/or apparatuses, may be implemented using
well known servers/computers, such as computer 900 shown in FIG.
8.
[0165] Computer 900 can be any commercially available and well
known computer capable of performing the functions described
herein, such as computers available from International Business
Machines, Apple, Sun, HP, Dell, Cray, etc. Computer 500 may be any
type of computer, including a desktop computer, a server, a tablet
computer, a a smart phone, etc.
[0166] As shown in FIG. 9, computer 900 includes one or more
processors (e.g., central processing units (CPUs)), such as
processor 906. Processor 906 may perform any of the functions or
steps described regarding FIGS. 1-8 in methods 200, 300, 400, 500,
600, 700, or 800 or any other calculation, estimation, or numerical
method described in this application herein. Processor 906 is
connected to a communication infrastructure 902, such as a
communication bus. In some embodiments, processor 906 can
simultaneously operate multiple computing threads.
[0167] Computer 900 also includes a primary or main memory 908,
such as a random access memory (RAM). Main memory has stored
therein control logic 924 (computer software), and data.
[0168] Computer 900 also includes one or more secondary storage
devices 910. Secondary storage devices 910 include, for example, a
hard disk drive 912 and/or a removable storage device or drive 914,
as well as other types of storage devices, such as memory cards and
memory sticks. For instance, computer 900 may include an industry
standard interface, such as a universal serial bus (USB) interface
for interfacing with devices such as a memory stick. Removable
storage drive 914 represents a floppy disk drive, a magnetic tape
drive, a compact disk drive, an optical storage device, tape
backup, etc.
[0169] Removable storage drive 914 interacts with a removable
storage unit 516. Removable storage unit 916 includes a computer
useable or readable storage medium 518 having stored therein
computer software 926 (control logic) and/or data. Removable
storage unit 916 represents a floppy disk, magnetic tape, compact
disc (CD), digital versatile disc (DVD), Blue-ray disc, optical
storage disk, memory stick, memory card, or any other computer data
storage device. Removable storage drive 914 reads from and/or
writes to removable storage unit 916 in a well-known manner.
[0170] Computer 900 also includes input/output/display devices 904,
such as monitors, keyboards, pointing devices, etc.
[0171] Computer 900 further includes a communication or network
interface 920. Communication interface 920 enables computer 900 to
communicate with remote devices. For example, communication
interface 920 allows computer 900 to communicate over communication
networks or mediums 922 (representing a form of a computer useable
or readable medium), such as local area networks (LANs), wide area
networks (WANs), the Internet, etc. Network interface 920 may
interface with remote sites or networks via wired or wireless
connections. Examples of communication interface 922 include but
are not limited to a modem, a network interface card (e.g., an
Ethernet card), a communication port, a Personal Computer Memory
Card International Association (PCMCIA) card, etc.
[0172] Control logic 928 may be transmitted to and from computer
900 via the communication medium 922.
[0173] The detailed descriptions of the above embodiments are not
exhaustive descriptions of all embodiments contemplated by the
inventors to be within the scope of the application. Indeed,
persons skilled in the art will recognize that certain elements of
the above-described embodiments may variously be combined or
eliminated to create further embodiments, and such further
embodiments fall within the scope and teachings of the application.
It will also be apparent to those of ordinary skill in the art that
the above-described embodiments may be combined in whole or in part
to create additional embodiments within the scope and teachings of
the application.
[0174] Thus, although specific embodiments of, and examples for,
the application are described herein for illustrative purposes,
various equivalent modifications are possible within the scope of
the application, as those skilled in the relevant art will
recognize. Accordingly, the scope of the application should be
determined from the following claims.
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